code: def test_jax_model_for_classification():
      tasks, dataset, transformers, metric = get_dataset(
          "classification", featurizer="ECFP"
      )
  
      # sample network
      class Encoder(hk.Module):
          def __init__(self, output_size: int = 2):
              super().__init__()
              self._network = hk.nets.MLP([512, 256, 128, output_size])
  
          def __call__(self, x: jnp.ndarray):
              x = self._network(x)
              return x, jax.nn.softmax(x)
  
      def bce_loss(pred, tar, w):
          tar = jnp.array(
              [
                  x.astype(np.float32) if x.dtype != np.float32 else x
                  for x in tar
              ]
          )
          return jnp.mean(optax.softmax_cross_entropy(pred[0], tar))
  
      # Model Initilisation
      params_init, forward_fn = hk.transform(
          lambda x: Encoder()(x)
      )  # noqa
      rng = jax.random.PRNGKey(500)
      inputs, _, _, _ = next(iter(dataset.iterbatches(batch_size=256)))
      modified_inputs = jnp.array(
          [
              x.astype(np.float32) if x.dtype == np.float64 else x
              for x in inputs
          ]
      )
      params = params_init(rng, modified_inputs)
  
      # Loss Function
      criterion = bce_loss
  
      # JaxModel Working
      j_m = JaxModel(
          forward_fn,
          params,
          criterion,
          output_types=["loss", "prediction"],
          batch_size=256,
          learning_rate=0.001,
          log_frequency=2,
      )
      _ = j_m.fit(dataset, nb_epochs=25, deterministic=True)
      scores = j_m.evaluate(dataset, [metric])
      assert scores[metric.name] > 0.8
  
file_path: deepchem/deepchem/models/jax_models/tests/test_jax_model.py
imports: import pytest
  from deepchem.models.tests.test_graph_models import get_dataset
  import deepchem
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: test_jax_model_for_classification() and returns: jnp, x,  jax